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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Journal of Applied Science, Engineering, Technology, and Education
Arlinta Christy Barus
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Performance Testing and Optimization of DiTenun Website Arlinta Christy Barus; Sinambela, Eka Stephani; Purba, Ivani; Simatupang, Jhonathan; Marpaung, Monika; Pandjaitan, Nancy
Journal of Applied Science, Engineering, Technology, and Education Vol. 4 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.003 KB) | DOI: 10.35877/454RI.asci841

Abstract

DiTenun website is a web-based application developed to create new motifs or new weaving patterns. This website needs to have good performance so that it can be optimally used by the users. By understanding the importance of the information available on the DiTenun website, a performance testing is necessary. This performance test aims to verify specific system performance such as response time and service availability despite receiving a large number of requests. The performance analysis of the DiTenun website was carried out by spike testing using the Apache JMeter tool. In solving the problems and improving the performance of the DiTenun website, the researcher applied website optimization by utilizing the browser cache, activating gzip compression, and eliminating rendering – blocking resources. Evaluation of each solution was carried out by using a hypothesis test, namely using a paired sample t test. The result of the test showed that each solution that was implemented can be used to improve performance in the form of load time of the DiTenun website.
Recognition Image Text Using Faster Region-Based Convolutional Neural Network with Optical Character Recognition Arie Satia Dharma; Arlinta Christy Barus; Samuel Herlinton Sibuea; Nanchy Monika Siadari
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.17700

Abstract

Business cards serve as a form of identification that facilitates communication, but managing large amounts of contact information on business cards can often be challenging. To address this issue, this study developed an end-to-end architecture model to automatically extract information from business cards image. This model utilizes Optical Character Recognition and the Faster Region-Based Convolutional Neural Network method. This model allows users to extract contact information from business cards. Using a dataset of 450 business card images, we conducted experiments to evaluate their impact on the task of detecting text in images. We used an image batch size of 500 with 50, 100, and 500 epochs as hyper experiment parameters. The highest accuracy achieved was 0.8342 with mAP was 0.8513. For the character recognition task, Optical Character Recognition produced results with a Character Error Rate (CER) less than 0.08. These findings suggest that the integration of Faster R-CNN and OCR is effective in detecting and extracting textual content from diverse business card layouts. In conclusion, the proposed approach provides a reliable and efficient solution for automated business card digitization and shows strong potential for practical applications in contact information management systems.
Co-Authors Arie Satia Dharma Monika Marpaung, Monika Nanchy Monika Siadari Pandjaitan, Nancy Purba, Ivani Samuel Herlinton Sibuea Simatupang, Jhonathan SINAMBELA, EKA STEPHANI